Endpoint for outpainting a video.
The client provides a convenient way to interact with the model API.
npm install --save @fal-ai/clientThe @fal-ai/serverless-client package has been deprecated in favor of @fal-ai/client. Install the new package and update your imports — see client setup.
Set FAL_KEY as an environment variable in your runtime.
export FAL_KEY="YOUR_API_KEY"The client API handles the API submit protocol. It will handle the request status updates and return the result when the request is completed.
import { fal } from "@fal-ai/client";
const result = await fal.subscribe("fal-ai/wan-vace-14b/outpainting", {
input: {
prompt: "A lone woman strides through the neon-drenched streets of Tokyo at night. Her crimson dress, a vibrant splash of color against the deep blues and blacks of the cityscape, flows slightly with each step. A tailored black jacket, crisp and elegant, contrasts sharply with the dress's rich texture. Medium shot: The city hums around her, blurred lights creating streaks of color in the background. Close-up: The fabric of her dress catches the streetlight's glow, revealing a subtle silk sheen and the intricate stitching at the hem. Her black jacket’s subtle texture is visible – a fine wool perhaps, with a matte finish. The overall mood is one of quiet confidence and mystery, a vibrant woman navigating a bustling, nocturnal landscape. High resolution 4k.",
video_url: "https://storage.googleapis.com/falserverless/web-examples/wan/t2v.mp4"
},
logs: true,
onQueueUpdate: (update) => {
if (update.status === "IN_PROGRESS") {
update.logs.map((log) => log.message).forEach(console.log);
}
},
});
console.log(result.data);
console.log(result.requestId);The API uses an API Key for authentication. It is recommended you set the FAL_KEY environment variable in your runtime when possible.
import { fal } from "@fal-ai/client";
fal.config({
credentials: "YOUR_FAL_KEY"
});When running code on the client-side (e.g. in a browser, mobile app or GUI applications), make sure to not expose your FAL_KEY. Instead, use a server-side proxy to make requests to the API. For more information, check out our server-side integration guide.
The client API provides a convenient way to submit requests to the model.
import { fal } from "@fal-ai/client";
const { request_id } = await fal.queue.submit("fal-ai/wan-vace-14b/outpainting", {
input: {
prompt: "A lone woman strides through the neon-drenched streets of Tokyo at night. Her crimson dress, a vibrant splash of color against the deep blues and blacks of the cityscape, flows slightly with each step. A tailored black jacket, crisp and elegant, contrasts sharply with the dress's rich texture. Medium shot: The city hums around her, blurred lights creating streaks of color in the background. Close-up: The fabric of her dress catches the streetlight's glow, revealing a subtle silk sheen and the intricate stitching at the hem. Her black jacket’s subtle texture is visible – a fine wool perhaps, with a matte finish. The overall mood is one of quiet confidence and mystery, a vibrant woman navigating a bustling, nocturnal landscape. High resolution 4k.",
video_url: "https://storage.googleapis.com/falserverless/web-examples/wan/t2v.mp4"
},
webhookUrl: "https://optional.webhook.url/for/results",
});You can fetch the status of a request to check if it is completed or still in progress.
import { fal } from "@fal-ai/client";
const status = await fal.queue.status("fal-ai/wan-vace-14b/outpainting", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b",
logs: true,
});Once the request is completed, you can fetch the result. See the Output Schema for the expected result format.
import { fal } from "@fal-ai/client";
const result = await fal.queue.result("fal-ai/wan-vace-14b/outpainting", {
requestId: "764cabcf-b745-4b3e-ae38-1200304cf45b"
});
console.log(result.data);
console.log(result.requestId);Some attributes in the API accept file URLs as input. Whenever that's the case you can pass your own URL or a Base64 data URI.
You can pass a Base64 data URI as a file input. The API will handle the file decoding for you. Keep in mind that for large files, this alternative although convenient can impact the request performance.
You can also pass your own URLs as long as they are publicly accessible. Be aware that some hosts might block cross-site requests, rate-limit, or consider the request as a bot.
We provide a convenient file storage that allows you to upload files and use them in your requests. You can upload files using the client API and use the returned URL in your requests.
import { fal } from "@fal-ai/client";
const file = new File(["Hello, World!"], "hello.txt", { type: "text/plain" });
const url = await fal.storage.upload(file);The client will auto-upload the file for you if you pass a binary object (e.g. File, Data).
Read more about file handling in our file upload guide.
prompt string* requiredThe text prompt to guide video generation.
negative_prompt stringNegative prompt for video generation. Default value: "letterboxing, borders, black bars, bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards"
match_input_num_frames booleanIf true, the number of frames in the generated video will match the number of frames in the input video. If false, the number of frames will be determined by the num_frames parameter.
num_frames integerNumber of frames to generate. Must be between 81 to 241 (inclusive). Default value: 81
match_input_frames_per_second booleanIf true, the frames per second of the generated video will match the input video. If false, the frames per second will be determined by the frames_per_second parameter.
frames_per_second integerFrames per second of the generated video. Must be between 5 to 30. Ignored if match_input_frames_per_second is true. Default value: 16
seed integerRandom seed for reproducibility. If None, a random seed is chosen.
resolution ResolutionEnumResolution of the generated video. Default value: "auto"
Possible enum values: auto, 240p, 360p, 480p, 580p, 720p
aspect_ratio AspectRatioEnumAspect ratio of the generated video. Default value: "auto"
Possible enum values: auto, 16:9, 1:1, 9:16
num_inference_steps integerNumber of inference steps for sampling. Higher values give better quality but take longer. Default value: 30
guidance_scale floatGuidance scale for classifier-free guidance. Higher values encourage the model to generate images closely related to the text prompt. Default value: 5
sampler SamplerEnumSampler to use for video generation. Default value: "unipc"
Possible enum values: unipc, dpm++, euler
shift floatShift parameter for video generation. Default value: 5
video_url string* requiredURL to the source video file. Required for outpainting.
URLs to source reference image. If provided, the model will use this image as reference.
first_frame_url stringURL to the first frame of the video. If provided, the model will use this frame as a reference.
last_frame_url stringURL to the last frame of the video. If provided, the model will use this frame as a reference.
enable_safety_checker booleanIf set to true, the safety checker will be enabled. Disabling it requires account authorization; unauthorized requests are always checked.
enable_prompt_expansion booleanWhether to enable prompt expansion.
acceleration EnumAcceleration to use for inference. Options are 'none' or 'regular'. Accelerated inference will very slightly affect output, but will be significantly faster. Default value: regular
Possible enum values: none, low, regular
video_quality VideoQualityEnumThe quality of the generated video. Default value: "high"
Possible enum values: low, medium, high, maximum
video_write_mode VideoWriteModeEnumThe write mode of the generated video. Default value: "balanced"
Possible enum values: fast, balanced, small
num_interpolated_frames integerNumber of frames to interpolate between the original frames. A value of 0 means no interpolation.
temporal_downsample_factor integerTemporal downsample factor for the video. This is an integer value that determines how many frames to skip in the video. A value of 0 means no downsampling. For each downsample factor, one upsample factor will automatically be applied.
enable_auto_downsample booleanIf true, the model will automatically temporally downsample the video to an appropriate frame length for the model, then will interpolate it back to the original frame length.
auto_downsample_min_fps floatThe minimum frames per second to downsample the video to. This is used to help determine the auto downsample factor to try and find the lowest detail-preserving downsample factor. The default value is appropriate for most videos, if you are using a video with very fast motion, you may need to increase this value. If your video has a very low amount of motion, you could decrease this value to allow for higher downsampling and thus longer sequences. Default value: 15
interpolator_model InterpolatorModelEnumThe model to use for frame interpolation. Options are 'rife' or 'film'. Default value: "film"
Possible enum values: rife, film
sync_mode booleanIf True, the media will be returned as a data URI and the output data won't be available in the request history.
transparency_mode TransparencyModeEnumThe transparency mode to apply to the first and last frames. This controls how the transparent areas of the first and last frames are filled. Default value: "content_aware"
Possible enum values: content_aware, white, black
return_frames_zip booleanIf true, also return a ZIP file containing all generated frames.
expand_left booleanWhether to expand the video to the left.
expand_right booleanWhether to expand the video to the right.
expand_top booleanWhether to expand the video to the top.
expand_bottom booleanWhether to expand the video to the bottom.
expand_ratio floatAmount of expansion. This is a float value between 0 and 1, where 0.25 adds 25% to the original video size on the specified sides. Default value: 0.25
{
"prompt": "A lone woman strides through the neon-drenched streets of Tokyo at night. Her crimson dress, a vibrant splash of color against the deep blues and blacks of the cityscape, flows slightly with each step. A tailored black jacket, crisp and elegant, contrasts sharply with the dress's rich texture. Medium shot: The city hums around her, blurred lights creating streaks of color in the background. Close-up: The fabric of her dress catches the streetlight's glow, revealing a subtle silk sheen and the intricate stitching at the hem. Her black jacket’s subtle texture is visible – a fine wool perhaps, with a matte finish. The overall mood is one of quiet confidence and mystery, a vibrant woman navigating a bustling, nocturnal landscape. High resolution 4k.",
"negative_prompt": "letterboxing, borders, black bars, bright colors, overexposed, static, blurred details, subtitles, style, artwork, painting, picture, still, overall gray, worst quality, low quality, JPEG compression residue, ugly, incomplete, extra fingers, poorly drawn hands, poorly drawn faces, deformed, disfigured, malformed limbs, fused fingers, still picture, cluttered background, three legs, many people in the background, walking backwards",
"match_input_num_frames": false,
"num_frames": 81,
"match_input_frames_per_second": false,
"frames_per_second": 16,
"resolution": "auto",
"aspect_ratio": "auto",
"num_inference_steps": 30,
"guidance_scale": 5,
"sampler": "unipc",
"shift": 5,
"video_url": "https://storage.googleapis.com/falserverless/web-examples/wan/t2v.mp4",
"enable_safety_checker": true,
"enable_prompt_expansion": false,
"acceleration": "regular",
"video_quality": "high",
"video_write_mode": "balanced",
"num_interpolated_frames": 0,
"temporal_downsample_factor": 0,
"enable_auto_downsample": false,
"auto_downsample_min_fps": 15,
"interpolator_model": "film",
"sync_mode": false,
"transparency_mode": "content_aware",
"return_frames_zip": false,
"expand_left": true,
"expand_right": true,
"expand_top": true,
"expand_bottom": true,
"expand_ratio": 0.25
}The generated outpainting video file.
prompt string* requiredThe prompt used for generation.
seed integer* requiredThe seed used for generation.
ZIP archive of all video frames if requested.
{
"video": {
"url": "https://storage.googleapis.com/falserverless/example_outputs/wan-vace-outpainting-output.mp4"
},
"prompt": ""
}url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.
url string* requiredThe URL where the file can be downloaded from.
content_type stringThe mime type of the file.
file_name stringThe name of the file. It will be auto-generated if not provided.
file_size integerThe size of the file in bytes.
width integerThe width of the video
height integerThe height of the video
fps floatThe FPS of the video
duration floatThe duration of the video
num_frames integerThe number of frames in the video